Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations114000
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.5 MiB
Average record size in memory161.0 B

Variable types

Numeric13
Text5
Boolean1
Categorical2

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
explicit is highly imbalanced (57.9%) Imbalance
time_signature is highly imbalanced (73.9%) Imbalance
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
popularity has 16020 (14.1%) zeros Zeros
key has 13061 (11.5%) zeros Zeros
instrumentalness has 38763 (34.0%) zeros Zeros

Reproduction

Analysis started2025-03-30 20:17:02.843033
Analysis finished2025-03-30 20:17:36.219072
Duration33.38 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct114000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56999.5
Minimum0
Maximum113999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:36.363491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5699.95
Q128499.75
median56999.5
Q385499.25
95-th percentile108299.05
Maximum113999
Range113999
Interquartile range (IQR)56999.5

Descriptive statistics

Standard deviation32909.11
Coefficient of variation (CV)0.57735787
Kurtosis-1.2
Mean56999.5
Median Absolute Deviation (MAD)28500
Skewness0
Sum6.497943 × 109
Variance1.0830095 × 109
MonotonicityStrictly increasing
2025-03-30T23:17:36.548010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
75997 1
 
< 0.1%
76008 1
 
< 0.1%
76007 1
 
< 0.1%
76006 1
 
< 0.1%
76005 1
 
< 0.1%
76004 1
 
< 0.1%
76003 1
 
< 0.1%
76002 1
 
< 0.1%
76001 1
 
< 0.1%
Other values (113990) 113990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
113999 1
< 0.1%
113998 1
< 0.1%
113997 1
< 0.1%
113996 1
< 0.1%
113995 1
< 0.1%
113994 1
< 0.1%
113993 1
< 0.1%
113992 1
< 0.1%
113991 1
< 0.1%
113990 1
< 0.1%
Distinct89741
Distinct (%)78.7%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:36.986966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2508000
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73100 ?
Unique (%)64.1%

Sample

1st row5SuOikwiRyPMVoIQDJUgSV
2nd row4qPNDBW1i3p13qLCt0Ki3A
3rd row1iJBSr7s7jYXzM8EGcbK5b
4th row6lfxq3CG4xtTiEg7opyCyx
5th row5vjLSffimiIP26QG5WcN2K
ValueCountFrequency (%)
6s3jldagk3uu3ntzbpnuhs 9
 
< 0.1%
2kkvb3rnrzwjfdghaua0tz 8
 
< 0.1%
2ey6v4sekh3z0rusisrosd 8
 
< 0.1%
4aqs25f3ywj9tgnnkoqilc 7
 
< 0.1%
5sqkarfxe7uejhtlcthcls 7
 
< 0.1%
6bzwr3epseolvwlblk58il 7
 
< 0.1%
54zcdkbialanv8ihi3xwld 7
 
< 0.1%
4xyiegksljlhpzb3bl6wmp 7
 
< 0.1%
5bi1xqmjk91dseq0bfe0ov 7
 
< 0.1%
5ftfvzslii5zxydnbrtf41 7
 
< 0.1%
Other values (89731) 113926
99.9%
2025-03-30T23:17:37.560874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2508000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2508000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2508000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 53778
 
2.1%
5 53497
 
2.1%
2 53335
 
2.1%
6 53275
 
2.1%
0 53232
 
2.1%
1 53162
 
2.1%
4 53152
 
2.1%
7 50535
 
2.0%
K 39217
 
1.6%
D 39104
 
1.6%
Other values (52) 2005713
80.0%
Distinct31437
Distinct (%)27.6%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2025-03-30T23:17:38.165966image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length513
Median length322
Mean length16.319354
Min length2

Characters and Unicode

Total characters1860390
Distinct characters712
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16767 ?
Unique (%)14.7%

Sample

1st rowGen Hoshino
2nd rowBen Woodward
3rd rowIngrid Michaelson;ZAYN
4th rowKina Grannis
5th rowChord Overstreet
ValueCountFrequency (%)
the 6831
 
2.6%
3126
 
1.2%
de 1133
 
0.4%
los 1066
 
0.4%
of 1034
 
0.4%
dj 738
 
0.3%
george 593
 
0.2%
jones 524
 
0.2%
la 518
 
0.2%
for 457
 
0.2%
Other values (42276) 241844
93.8%
2025-03-30T23:17:39.082294image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 164229
 
8.8%
e 148733
 
8.0%
143873
 
7.7%
i 112151
 
6.0%
n 106549
 
5.7%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.7%
t 63612
 
3.4%
Other values (702) 772182
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1860390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 164229
 
8.8%
e 148733
 
8.0%
143873
 
7.7%
i 112151
 
6.0%
n 106549
 
5.7%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.7%
t 63612
 
3.4%
Other values (702) 772182
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1860390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 164229
 
8.8%
e 148733
 
8.0%
143873
 
7.7%
i 112151
 
6.0%
n 106549
 
5.7%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.7%
t 63612
 
3.4%
Other values (702) 772182
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1860390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 164229
 
8.8%
e 148733
 
8.0%
143873
 
7.7%
i 112151
 
6.0%
n 106549
 
5.7%
o 103832
 
5.6%
r 100226
 
5.4%
l 75690
 
4.1%
s 69313
 
3.7%
t 63612
 
3.4%
Other values (702) 772182
41.5%
Distinct46589
Distinct (%)40.9%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2025-03-30T23:17:39.775986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length243
Median length145
Mean length20.116668
Min length1

Characters and Unicode

Total characters2293280
Distinct characters2084
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27955 ?
Unique (%)24.5%

Sample

1st rowComedy
2nd rowGhost (Acoustic)
3rd rowTo Begin Again
4th rowCrazy Rich Asians (Original Motion Picture Soundtrack)
5th rowHold On
ValueCountFrequency (%)
the 12029
 
3.1%
9198
 
2.3%
of 5240
 
1.3%
2022 3430
 
0.9%
vol 3257
 
0.8%
christmas 3214
 
0.8%
vivo 3186
 
0.8%
a 3174
 
0.8%
ao 2929
 
0.7%
de 2893
 
0.7%
Other values (35981) 343235
87.6%
2025-03-30T23:17:40.744458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277786
 
12.1%
e 184978
 
8.1%
a 142803
 
6.2%
o 138424
 
6.0%
i 127748
 
5.6%
n 106159
 
4.6%
r 105849
 
4.6%
s 96731
 
4.2%
t 96378
 
4.2%
l 79067
 
3.4%
Other values (2074) 937357
40.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2293280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
277786
 
12.1%
e 184978
 
8.1%
a 142803
 
6.2%
o 138424
 
6.0%
i 127748
 
5.6%
n 106159
 
4.6%
r 105849
 
4.6%
s 96731
 
4.2%
t 96378
 
4.2%
l 79067
 
3.4%
Other values (2074) 937357
40.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2293280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
277786
 
12.1%
e 184978
 
8.1%
a 142803
 
6.2%
o 138424
 
6.0%
i 127748
 
5.6%
n 106159
 
4.6%
r 105849
 
4.6%
s 96731
 
4.2%
t 96378
 
4.2%
l 79067
 
3.4%
Other values (2074) 937357
40.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2293280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
277786
 
12.1%
e 184978
 
8.1%
a 142803
 
6.2%
o 138424
 
6.0%
i 127748
 
5.6%
n 106159
 
4.6%
r 105849
 
4.6%
s 96731
 
4.2%
t 96378
 
4.2%
l 79067
 
3.4%
Other values (2074) 937357
40.9%
Distinct73608
Distinct (%)64.6%
Missing1
Missing (%)< 0.1%
Memory size890.8 KiB
2025-03-30T23:17:41.681948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length511
Median length146
Mean length17.994684
Min length1

Characters and Unicode

Total characters2051376
Distinct characters2417
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55711 ?
Unique (%)48.9%

Sample

1st rowComedy
2nd rowGhost - Acoustic
3rd rowTo Begin Again
4th rowCan't Help Falling In Love
5th rowHold On
ValueCountFrequency (%)
19654
 
5.1%
the 9471
 
2.5%
you 4292
 
1.1%
me 3716
 
1.0%
a 3696
 
1.0%
of 3605
 
0.9%
i 3409
 
0.9%
in 3180
 
0.8%
vivo 3158
 
0.8%
remix 2984
 
0.8%
Other values (50550) 328614
85.2%
2025-03-30T23:17:42.689132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136251
 
6.6%
o 122444
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92079
 
4.5%
t 81895
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837738
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2051376
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136251
 
6.6%
o 122444
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92079
 
4.5%
t 81895
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837738
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2051376
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136251
 
6.6%
o 122444
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92079
 
4.5%
t 81895
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837738
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2051376
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
271780
 
13.2%
e 174853
 
8.5%
a 136251
 
6.6%
o 122444
 
6.0%
i 109433
 
5.3%
n 94021
 
4.6%
r 92079
 
4.5%
t 81895
 
4.0%
s 67733
 
3.3%
l 63149
 
3.1%
Other values (2407) 837738
40.8%

popularity
Real number (ℝ)

Zeros 

Distinct101
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.238535
Minimum0
Maximum100
Zeros16020
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:42.900608image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117
median35
Q350
95-th percentile69
Maximum100
Range100
Interquartile range (IQR)33

Descriptive statistics

Standard deviation22.305078
Coefficient of variation (CV)0.67106082
Kurtosis-0.92775532
Mean33.238535
Median Absolute Deviation (MAD)16
Skewness0.046402516
Sum3789193
Variance497.51653
MonotonicityNot monotonic
2025-03-30T23:17:43.117479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16020
 
14.1%
22 2354
 
2.1%
21 2344
 
2.1%
44 2288
 
2.0%
1 2140
 
1.9%
23 2117
 
1.9%
20 2110
 
1.9%
43 2073
 
1.8%
45 2004
 
1.8%
41 1996
 
1.8%
Other values (91) 78554
68.9%
ValueCountFrequency (%)
0 16020
14.1%
1 2140
 
1.9%
2 1036
 
0.9%
3 585
 
0.5%
4 389
 
0.3%
5 599
 
0.5%
6 426
 
0.4%
7 465
 
0.4%
8 544
 
0.5%
9 525
 
0.5%
ValueCountFrequency (%)
100 2
 
< 0.1%
99 1
 
< 0.1%
98 7
< 0.1%
97 8
< 0.1%
96 7
< 0.1%
95 5
< 0.1%
94 7
< 0.1%
93 12
< 0.1%
92 9
< 0.1%
91 10
< 0.1%

duration_ms
Real number (ℝ)

Distinct50697
Distinct (%)44.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228029.15
Minimum0
Maximum5237295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:43.322404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116920
Q1174066
median212906
Q3261506
95-th percentile387167.1
Maximum5237295
Range5237295
Interquartile range (IQR)87440

Descriptive statistics

Standard deviation107297.71
Coefficient of variation (CV)0.47054384
Kurtosis354.95242
Mean228029.15
Median Absolute Deviation (MAD)42760
Skewness11.195181
Sum2.5995323 × 1010
Variance1.1512799 × 1010
MonotonicityNot monotonic
2025-03-30T23:17:43.508165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
162897 146
 
0.1%
180000 104
 
0.1%
192000 91
 
0.1%
240000 84
 
0.1%
118840 76
 
0.1%
172342 75
 
0.1%
227520 71
 
0.1%
131733 70
 
0.1%
243057 66
 
0.1%
175986 63
 
0.1%
Other values (50687) 113154
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
8586 1
< 0.1%
13386 1
< 0.1%
15800 1
< 0.1%
17453 1
< 0.1%
17826 2
< 0.1%
21120 1
< 0.1%
21240 1
< 0.1%
22266 1
< 0.1%
23506 2
< 0.1%
ValueCountFrequency (%)
5237295 1
< 0.1%
4789026 2
< 0.1%
4730302 1
< 0.1%
4563897 1
< 0.1%
4447520 1
< 0.1%
4339826 1
< 0.1%
4334721 1
< 0.1%
4246206 1
< 0.1%
4120258 1
< 0.1%
3876276 2
< 0.1%

explicit
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size111.5 KiB
False
104253 
True
 
9747
ValueCountFrequency (%)
False 104253
91.5%
True 9747
 
8.6%
2025-03-30T23:17:43.662750image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

danceability
Real number (ℝ)

Distinct1174
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56680007
Minimum0
Maximum0.985
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:43.826273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.456
median0.58
Q30.695
95-th percentile0.824
Maximum0.985
Range0.985
Interquartile range (IQR)0.239

Descriptive statistics

Standard deviation0.17354217
Coefficient of variation (CV)0.30617882
Kurtosis-0.18450245
Mean0.56680007
Median Absolute Deviation (MAD)0.119
Skewness-0.39949663
Sum64615.208
Variance0.030116886
MonotonicityNot monotonic
2025-03-30T23:17:44.028730image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.647 431
 
0.4%
0.609 357
 
0.3%
0.579 347
 
0.3%
0.685 335
 
0.3%
0.602 334
 
0.3%
0.524 317
 
0.3%
0.689 315
 
0.3%
0.598 312
 
0.3%
0.607 307
 
0.3%
0.626 306
 
0.3%
Other values (1164) 110639
97.1%
ValueCountFrequency (%)
0 157
0.1%
0.0513 1
 
< 0.1%
0.0532 1
 
< 0.1%
0.0545 1
 
< 0.1%
0.0548 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 1
 
< 0.1%
0.0558 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0565 2
 
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 2
< 0.1%
0.98 2
< 0.1%
0.979 2
< 0.1%
0.978 3
< 0.1%
0.977 1
 
< 0.1%
0.976 4
< 0.1%

energy
Real number (ℝ)

High correlation 

Distinct2083
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64138276
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:44.212239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.154
Q10.472
median0.685
Q30.854
95-th percentile0.969
Maximum1
Range1
Interquartile range (IQR)0.382

Descriptive statistics

Standard deviation0.25152907
Coefficient of variation (CV)0.39216687
Kurtosis-0.52571082
Mean0.64138276
Median Absolute Deviation (MAD)0.186
Skewness-0.59700142
Sum73117.634
Variance0.063266872
MonotonicityNot monotonic
2025-03-30T23:17:44.393753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.876 318
 
0.3%
0.937 269
 
0.2%
0.931 261
 
0.2%
0.886 258
 
0.2%
0.801 258
 
0.2%
0.948 254
 
0.2%
0.858 254
 
0.2%
0.961 254
 
0.2%
0.92 240
 
0.2%
0.981 238
 
0.2%
Other values (2073) 111396
97.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1.95 × 10-51
 
< 0.1%
2.01 × 10-513
 
< 0.1%
2.02 × 10-54
 
< 0.1%
2.03 × 10-534
< 0.1%
2.82 × 10-51
 
< 0.1%
3.05 × 10-51
 
< 0.1%
3.61 × 10-51
 
< 0.1%
4.28 × 10-53
 
< 0.1%
5.9 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 28
 
< 0.1%
0.999 100
0.1%
0.998 149
0.1%
0.997 165
0.1%
0.996 159
0.1%
0.995 229
0.2%
0.994 173
0.2%
0.993 184
0.2%
0.992 161
0.1%
0.991 200
0.2%

key
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3091404
Minimum0
Maximum11
Zeros13061
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:44.538443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5599871
Coefficient of variation (CV)0.67053928
Kurtosis-1.2765712
Mean5.3091404
Median Absolute Deviation (MAD)3
Skewness-0.0085003605
Sum605242
Variance12.673508
MonotonicityNot monotonic
2025-03-30T23:17:44.935470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 13245
11.6%
0 13061
11.5%
2 11644
10.2%
9 11313
9.9%
1 10772
9.4%
5 9368
8.2%
11 9282
8.1%
4 9008
7.9%
6 7921
6.9%
10 7456
6.5%
Other values (2) 10930
9.6%
ValueCountFrequency (%)
0 13061
11.5%
1 10772
9.4%
2 11644
10.2%
3 3570
 
3.1%
4 9008
7.9%
5 9368
8.2%
6 7921
6.9%
7 13245
11.6%
8 7360
6.5%
9 11313
9.9%
ValueCountFrequency (%)
11 9282
8.1%
10 7456
6.5%
9 11313
9.9%
8 7360
6.5%
7 13245
11.6%
6 7921
6.9%
5 9368
8.2%
4 9008
7.9%
3 3570
 
3.1%
2 11644
10.2%

loudness
Real number (ℝ)

High correlation 

Distinct19480
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2589604
Minimum-49.531
Maximum4.532
Zeros0
Zeros (%)0.0%
Negative113910
Negative (%)99.9%
Memory size890.8 KiB
2025-03-30T23:17:45.095567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-49.531
5-th percentile-18.067
Q1-10.013
median-7.004
Q3-5.003
95-th percentile-2.974
Maximum4.532
Range54.063
Interquartile range (IQR)5.01

Descriptive statistics

Standard deviation5.0293366
Coefficient of variation (CV)-0.60895517
Kurtosis5.8962782
Mean-8.2589604
Median Absolute Deviation (MAD)2.343
Skewness-2.0065419
Sum-941521.48
Variance25.294227
MonotonicityNot monotonic
2025-03-30T23:17:45.282246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.662 176
 
0.2%
-4.457 90
 
0.1%
-9.336 86
 
0.1%
-7.57 77
 
0.1%
-4.034 75
 
0.1%
-8.871 74
 
0.1%
-3.725 72
 
0.1%
-4.324 70
 
0.1%
-5.08 64
 
0.1%
-12.472 64
 
0.1%
Other values (19470) 113152
99.3%
ValueCountFrequency (%)
-49.531 1
 
< 0.1%
-49.307 1
 
< 0.1%
-46.591 1
 
< 0.1%
-46.251 1
 
< 0.1%
-43.957 1
 
< 0.1%
-43.943 1
 
< 0.1%
-43.714 1
 
< 0.1%
-43.504 1
 
< 0.1%
-43.303 1
 
< 0.1%
-43.046 3
< 0.1%
ValueCountFrequency (%)
4.532 1
< 0.1%
3.156 1
< 0.1%
2.574 1
< 0.1%
1.864 1
< 0.1%
1.821 1
< 0.1%
1.795 1
< 0.1%
1.7 1
< 0.1%
1.682 1
< 0.1%
1.673 1
< 0.1%
1.416 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
1
72681 
0
41319 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters114000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Length

2025-03-30T23:17:45.441345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:17:45.554545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring characters

ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 72681
63.8%
0 41319
36.2%

speechiness
Real number (ℝ)

Distinct1489
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.084652112
Minimum0
Maximum0.965
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:45.707380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0282
Q10.0359
median0.0489
Q30.0845
95-th percentile0.268
Maximum0.965
Range0.965
Interquartile range (IQR)0.0486

Descriptive statistics

Standard deviation0.10573236
Coefficient of variation (CV)1.2490222
Kurtosis28.824377
Mean0.084652112
Median Absolute Deviation (MAD)0.0165
Skewness4.647516
Sum9650.3408
Variance0.011179333
MonotonicityNot monotonic
2025-03-30T23:17:45.903833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0323 400
 
0.4%
0.0324 376
 
0.3%
0.0322 373
 
0.3%
0.0328 363
 
0.3%
0.0295 358
 
0.3%
0.0321 352
 
0.3%
0.033 347
 
0.3%
0.0367 346
 
0.3%
0.0326 340
 
0.3%
0.0306 332
 
0.3%
Other values (1479) 110413
96.9%
ValueCountFrequency (%)
0 157
0.1%
0.0221 3
 
< 0.1%
0.0222 1
 
< 0.1%
0.0223 3
 
< 0.1%
0.0225 2
 
< 0.1%
0.0226 2
 
< 0.1%
0.0227 3
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 1
 
< 0.1%
0.023 9
 
< 0.1%
ValueCountFrequency (%)
0.965 1
 
< 0.1%
0.963 2
 
< 0.1%
0.962 6
< 0.1%
0.961 2
 
< 0.1%
0.96 3
 
< 0.1%
0.959 6
< 0.1%
0.958 6
< 0.1%
0.957 8
< 0.1%
0.956 7
< 0.1%
0.955 11
< 0.1%

acousticness
Real number (ℝ)

High correlation 

Distinct5061
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31491006
Minimum0
Maximum0.996
Zeros39
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:46.103956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000145
Q10.0169
median0.169
Q30.598
95-th percentile0.948
Maximum0.996
Range0.996
Interquartile range (IQR)0.5811

Descriptive statistics

Standard deviation0.3325227
Coefficient of variation (CV)1.0559291
Kurtosis-0.94993129
Mean0.31491006
Median Absolute Deviation (MAD)0.1675
Skewness0.72729486
Sum35899.747
Variance0.11057135
MonotonicityNot monotonic
2025-03-30T23:17:46.306518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 305
 
0.3%
0.993 267
 
0.2%
0.994 266
 
0.2%
0.992 250
 
0.2%
0.991 218
 
0.2%
0.131 206
 
0.2%
0.881 204
 
0.2%
0.108 195
 
0.2%
0.107 190
 
0.2%
0.99 189
 
0.2%
Other values (5051) 111710
98.0%
ValueCountFrequency (%)
0 39
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-61
 
< 0.1%
1.03 × 10-62
 
< 0.1%
1.04 × 10-64
 
< 0.1%
1.06 × 10-65
 
< 0.1%
1.07 × 10-64
 
< 0.1%
1.08 × 10-62
 
< 0.1%
1.09 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 103
 
0.1%
0.995 305
0.3%
0.994 266
0.2%
0.993 267
0.2%
0.992 250
0.2%
0.991 218
0.2%
0.99 189
0.2%
0.989 177
0.2%
0.988 150
0.1%
0.987 158
0.1%

instrumentalness
Real number (ℝ)

Zeros 

Distinct5346
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15604959
Minimum0
Maximum1
Zeros38763
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:46.507187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.16 × 10-5
Q30.049
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.049

Descriptive statistics

Standard deviation0.30955485
Coefficient of variation (CV)1.9836954
Kurtosis1.2707471
Mean0.15604959
Median Absolute Deviation (MAD)4.16 × 10-5
Skewness1.7344062
Sum17789.653
Variance0.095824204
MonotonicityNot monotonic
2025-03-30T23:17:46.688889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38763
34.0%
3.59 × 10-5166
 
0.1%
0.895 122
 
0.1%
0.905 122
 
0.1%
0.934 121
 
0.1%
0.922 118
 
0.1%
0.911 115
 
0.1%
0.000141 115
 
0.1%
0.913 114
 
0.1%
0.9 114
 
0.1%
Other values (5336) 74130
65.0%
ValueCountFrequency (%)
0 38763
34.0%
1 × 10-632
 
< 0.1%
1.01 × 10-646
 
< 0.1%
1.02 × 10-636
 
< 0.1%
1.03 × 10-634
 
< 0.1%
1.04 × 10-650
 
< 0.1%
1.05 × 10-639
 
< 0.1%
1.06 × 10-649
 
< 0.1%
1.07 × 10-656
 
< 0.1%
1.08 × 10-647
 
< 0.1%
ValueCountFrequency (%)
1 13
< 0.1%
0.999 22
< 0.1%
0.998 6
 
< 0.1%
0.997 11
< 0.1%
0.996 4
 
< 0.1%
0.995 15
< 0.1%
0.994 4
 
< 0.1%
0.993 9
< 0.1%
0.992 11
< 0.1%
0.991 12
< 0.1%

liveness
Real number (ℝ)

Distinct1722
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21355284
Minimum0
Maximum1
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:46.866414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0606
Q10.098
median0.132
Q30.273
95-th percentile0.681
Maximum1
Range1
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1903777
Coefficient of variation (CV)0.89147821
Kurtosis4.3782683
Mean0.21355284
Median Absolute Deviation (MAD)0.051
Skewness2.1057381
Sum24345.023
Variance0.036243668
MonotonicityNot monotonic
2025-03-30T23:17:47.042927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.108 1353
 
1.2%
0.111 1318
 
1.2%
0.109 1198
 
1.1%
0.11 1179
 
1.0%
0.105 1114
 
1.0%
0.107 1102
 
1.0%
0.103 1094
 
1.0%
0.106 1064
 
0.9%
0.112 1063
 
0.9%
0.113 1008
 
0.9%
Other values (1712) 102507
89.9%
ValueCountFrequency (%)
0 2
< 0.1%
0.00925 1
< 0.1%
0.00986 1
< 0.1%
0.0112 1
< 0.1%
0.0114 1
< 0.1%
0.0116 1
< 0.1%
0.0118 1
< 0.1%
0.0133 1
< 0.1%
0.0136 1
< 0.1%
0.0137 1
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.997 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 3
 
< 0.1%
0.993 2
 
< 0.1%
0.992 9
< 0.1%
0.991 4
 
< 0.1%
0.99 11
< 0.1%
0.989 17
< 0.1%
0.988 17
< 0.1%

valence
Real number (ℝ)

Distinct1790
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47406823
Minimum0
Maximum0.995
Zeros176
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:47.216503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0708
Q10.26
median0.464
Q30.683
95-th percentile0.911
Maximum0.995
Range0.995
Interquartile range (IQR)0.423

Descriptive statistics

Standard deviation0.25926106
Coefficient of variation (CV)0.54688555
Kurtosis-1.0274297
Mean0.47406823
Median Absolute Deviation (MAD)0.212
Skewness0.11507804
Sum54043.778
Variance0.0672163
MonotonicityNot monotonic
2025-03-30T23:17:47.401818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 300
 
0.3%
0.304 248
 
0.2%
0.717 233
 
0.2%
0.962 230
 
0.2%
0.324 225
 
0.2%
0.963 216
 
0.2%
0.55 210
 
0.2%
0.365 205
 
0.2%
0.949 204
 
0.2%
0.202 201
 
0.2%
Other values (1780) 111728
98.0%
ValueCountFrequency (%)
0 176
0.2%
1 × 10-5129
0.1%
0.000322 1
 
< 0.1%
0.000378 1
 
< 0.1%
0.000667 1
 
< 0.1%
0.000673 1
 
< 0.1%
0.000755 1
 
< 0.1%
0.000781 1
 
< 0.1%
0.00084 1
 
< 0.1%
0.000885 1
 
< 0.1%
ValueCountFrequency (%)
0.995 1
 
< 0.1%
0.994 1
 
< 0.1%
0.993 3
< 0.1%
0.992 4
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 4
< 0.1%
0.987 2
< 0.1%
0.986 1
 
< 0.1%

tempo
Real number (ℝ)

Distinct45653
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.14784
Minimum0
Maximum243.372
Zeros157
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:47.581547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.3469
Q199.21875
median122.017
Q3140.071
95-th percentile175.06715
Maximum243.372
Range243.372
Interquartile range (IQR)40.85225

Descriptive statistics

Standard deviation29.978197
Coefficient of variation (CV)0.24542552
Kurtosis-0.10858061
Mean122.14784
Median Absolute Deviation (MAD)21.7025
Skewness0.23229486
Sum13924853
Variance898.69229
MonotonicityNot monotonic
2025-03-30T23:17:47.769629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 157
 
0.1%
151.925 146
 
0.1%
95.004 95
 
0.1%
87.925 76
 
0.1%
130.594 76
 
0.1%
92.988 70
 
0.1%
125.004 70
 
0.1%
76.783 69
 
0.1%
77.321 67
 
0.1%
90.04 63
 
0.1%
Other values (45643) 113111
99.2%
ValueCountFrequency (%)
0 157
0.1%
30.2 1
 
< 0.1%
30.322 1
 
< 0.1%
31.834 1
 
< 0.1%
34.262 1
 
< 0.1%
34.821 1
 
< 0.1%
35.392 1
 
< 0.1%
35.79 1
 
< 0.1%
35.862 1
 
< 0.1%
35.928 1
 
< 0.1%
ValueCountFrequency (%)
243.372 1
 
< 0.1%
222.605 1
 
< 0.1%
220.525 1
 
< 0.1%
220.084 1
 
< 0.1%
220.081 3
< 0.1%
220.039 1
 
< 0.1%
219.971 1
 
< 0.1%
219.693 1
 
< 0.1%
219.571 1
 
< 0.1%
218.879 1
 
< 0.1%

time_signature
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
4
101843 
3
 
9195
5
 
1826
1
 
973
0
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters114000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row3
5th row4

Common Values

ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Length

2025-03-30T23:17:47.952798image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-30T23:17:48.082446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 114000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 101843
89.3%
3 9195
 
8.1%
5 1826
 
1.6%
1 973
 
0.9%
0 163
 
0.1%
Distinct114
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size890.8 KiB
2025-03-30T23:17:48.551231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length17
Median length11
Mean length7.0701754
Min length3

Characters and Unicode

Total characters806000
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowacoustic
2nd rowacoustic
3rd rowacoustic
4th rowacoustic
5th rowacoustic
ValueCountFrequency (%)
acoustic 1000
 
0.9%
drum-and-bass 1000
 
0.9%
alternative 1000
 
0.9%
ambient 1000
 
0.9%
anime 1000
 
0.9%
black-metal 1000
 
0.9%
bluegrass 1000
 
0.9%
blues 1000
 
0.9%
brazil 1000
 
0.9%
breakbeat 1000
 
0.9%
Other values (104) 104000
91.2%
2025-03-30T23:17:49.220435image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 806000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 806000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 806000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 73000
 
9.1%
a 68000
 
8.4%
o 67000
 
8.3%
r 57000
 
7.1%
n 50000
 
6.2%
i 47000
 
5.8%
s 44000
 
5.5%
t 43000
 
5.3%
p 39000
 
4.8%
l 39000
 
4.8%
Other values (15) 279000
34.6%

Interactions

2025-03-30T23:17:33.155512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:10.871547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.744514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.463107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.381044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.371769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.288348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.081944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.933710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.075361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.761083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.533971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.256359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.282213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.037507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.877155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.596792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.540728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.514402image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.427899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.241533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.065357image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.202667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.915606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.668651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.382508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.425708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.169492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.025956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.724414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.679359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.657047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.569720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.385173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.194216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.330328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.060260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.816254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.507161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.554492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.300138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.160974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.964574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.827532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.790624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.697502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.525796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.328854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.458408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.203100image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.942913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.643022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.686218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.537001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.290236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.114537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.969757image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.924305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.833385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.670411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.455483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.587348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.346728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.067737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.770680image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.819860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.665809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.418243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.252239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.132583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.051577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.957255image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.807550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.605579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.710260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.478642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.194626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.114792image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.950512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.796458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.545187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.392663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.280187image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.180892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.090602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:22.955205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.825989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.836922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.613287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.343230image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.238426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.095835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:11.926903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.672203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.539271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.431348image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.332492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.216790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.088839image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:24.989586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:26.966085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.739949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.492829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.370683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.226530image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.059512image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.803782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.677502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.596082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.470354image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.348448image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.213656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:25.113257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.090206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.865799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.631583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.501184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.358615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.202927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:13.931528image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.821117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.742832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.755450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.476773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.349716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:25.491243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.217216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:28.990465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.756301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.632901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.494091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.340776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.065171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:15.961703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:17.924559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:19.884310image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.605392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.488861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:25.645924image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.343269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.117089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:30.879056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.779471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.627748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.484020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.192869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.095432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.071196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.020412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.742584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.653423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:25.796556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.481370image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.260741image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.002432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:32.906836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:34.749421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:12.614850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:14.326509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:16.222158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:18.208025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:20.156523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:21.910405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:23.794043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:25.933191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:27.623428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:29.399331image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:31.127611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-30T23:17:33.030681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-30T23:17:49.466779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Unnamed: 0acousticnessdanceabilityduration_msenergyexplicitinstrumentalnesskeylivenessloudnessmodepopularityspeechinesstempotime_signaturevalence
Unnamed: 01.0000.1000.007-0.029-0.0640.102-0.084-0.0060.027-0.0430.0720.033-0.042-0.0250.0700.053
acousticness0.1001.000-0.039-0.170-0.7080.102-0.096-0.038-0.042-0.5340.1000.008-0.214-0.2170.141-0.021
danceability0.007-0.0391.000-0.0980.0390.154-0.1440.035-0.1450.1120.0850.0270.159-0.0710.2790.462
duration_ms-0.029-0.170-0.0981.0000.1040.0110.1270.014-0.0400.0220.0040.028-0.1290.0500.036-0.178
energy-0.064-0.7080.0390.1041.0000.116-0.0350.0450.1770.7500.087-0.0240.3550.2410.1610.208
explicit0.1020.1020.1540.0110.1161.0000.1040.0400.0420.1080.0370.0890.3060.0400.0600.069
instrumentalness-0.084-0.096-0.1440.127-0.0350.1041.0000.005-0.099-0.2890.059-0.078-0.049-0.0050.067-0.320
key-0.006-0.0380.0350.0140.0450.0400.0051.000-0.0040.0320.247-0.0030.0440.0120.0210.033
liveness0.027-0.042-0.145-0.0400.1770.042-0.099-0.0041.0000.1110.029-0.0080.0920.0190.0400.013
loudness-0.043-0.5340.1120.0220.7500.108-0.2890.0320.1111.0000.0450.0350.2320.1940.1520.221
mode0.0720.1000.0850.0040.0870.0370.0590.2470.0290.0451.0000.0370.0670.0260.0280.033
popularity0.0330.0080.0270.028-0.0240.089-0.078-0.003-0.0080.0350.0371.000-0.0680.0170.046-0.042
speechiness-0.042-0.2140.159-0.1290.3550.306-0.0490.0440.0920.2320.067-0.0681.0000.1150.0850.092
tempo-0.025-0.217-0.0710.0500.2410.040-0.0050.0120.0190.1940.0260.0170.1151.0000.4960.063
time_signature0.0700.1410.2790.0360.1610.0600.0670.0210.0400.1520.0280.0460.0850.4961.0000.111
valence0.053-0.0210.462-0.1780.2080.069-0.3200.0330.0130.2210.033-0.0420.0920.0630.1111.000

Missing values

2025-03-30T23:17:34.962460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-30T23:17:35.458566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-30T23:17:35.968603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
005SuOikwiRyPMVoIQDJUgSVGen HoshinoComedyComedy73230666False0.6760.46101-6.74600.14300.03220.0000010.35800.715087.9174acoustic
114qPNDBW1i3p13qLCt0Ki3ABen WoodwardGhost (Acoustic)Ghost - Acoustic55149610False0.4200.16601-17.23510.07630.92400.0000060.10100.267077.4894acoustic
221iJBSr7s7jYXzM8EGcbK5bIngrid Michaelson;ZAYNTo Begin AgainTo Begin Again57210826False0.4380.35900-9.73410.05570.21000.0000000.11700.120076.3324acoustic
336lfxq3CG4xtTiEg7opyCyxKina GrannisCrazy Rich Asians (Original Motion Picture Soundtrack)Can't Help Falling In Love71201933False0.2660.05960-18.51510.03630.90500.0000710.13200.1430181.7403acoustic
445vjLSffimiIP26QG5WcN2KChord OverstreetHold OnHold On82198853False0.6180.44302-9.68110.05260.46900.0000000.08290.1670119.9494acoustic
5501MVOl9KtVTNfFiBU9I7dcTyrone WellsDays I Will RememberDays I Will Remember58214240False0.6880.48106-8.80710.10500.28900.0000000.18900.666098.0174acoustic
666Vc5wAMmXdKIAM7WUoEb7NA Great Big World;Christina AguileraIs There Anybody Out There?Say Something74229400False0.4070.14702-8.82210.03550.85700.0000030.09130.0765141.2843acoustic
771EzrEOXmMH3G43AXT1y7pAJason MrazWe Sing. We Dance. We Steal Things.I'm Yours80242946False0.7030.444011-9.33110.04170.55900.0000000.09730.7120150.9604acoustic
880IktbUcnAGrvD03AWnz3Q8Jason Mraz;Colbie CaillatWe Sing. We Dance. We Steal Things.Lucky74189613False0.6250.41400-8.70010.03690.29400.0000000.15100.6690130.0884acoustic
997k9GuJYLp2AzqokyEdwEw2Ross CoppermanHungerHunger56205594False0.4420.63201-6.77010.02950.42600.0041900.07350.196078.8994acoustic
Unnamed: 0track_idartistsalbum_nametrack_namepopularityduration_msexplicitdanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempotime_signaturetrack_genre
1139901139902A4dSiJmbviL56CBupkh6CLucas CervettiFrecuencias Álmicas en 432hz (Solo Piano)Frecuencia Álmica XI - Solo Piano22369049False0.5790.2454-16.35710.03840.970000.9240000.10100.3020112.0113world-music
1139911139910CE0Y6GM75cbrqao8EOAlWChris TomlinThe Ultimate PlaylistAt The Cross (Love Ran Red)32250629False0.3870.5318-4.78810.02900.003050.0000000.20100.1530146.0034world-music
1139921139923FjOBB4EyIXHYUtSgrIdY9Jesus CultureRevelation SongsYour Love Never Fails38312566False0.4750.86010-4.72210.04210.006500.0000020.24600.4270113.9494world-music
1139931139934OkMK49i3NApR1KsAIsTf6Chris TomlinSee The Morning (Special Edition)How Can I Keep From Singing39256026False0.5050.68710-4.37510.02870.084100.0000000.18800.3820104.0833world-music
1139941139944WbOUe6T0sozC7z5ZJgiAALucas CervettiFrecuencias Álmicas en 432hzFrecuencia Álmica, Pt. 422305454False0.3310.1711-15.66810.03500.920000.0229000.06790.3270132.1473world-music
1139951139952C3TZjDRiAzdyViavDJ217Rainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicSleep My Little Boy21384999False0.1720.2355-16.39310.04220.640000.9280000.08630.0339125.9955world-music
1139961139961hIz5L4IB9hN3WRYPOCGPwRainy Lullaby#mindfulness - Soft Rain for Mindful Meditation, Stress Relief Relaxation MusicWater Into Light22385000False0.1740.1170-18.31800.04010.994000.9760000.10500.035085.2394world-music
1139971139976x8ZfSoqDjuNa5SVP5QjvXCesária EvoraBest OfMiss Perfumado22271466False0.6290.3290-10.89500.04200.867000.0000000.08390.7430132.3784world-music
1139981139982e6sXL2bYv4bSz6VTdnfLsMichael W. SmithChange Your WorldFriends41283893False0.5870.5067-10.88910.02970.381000.0000000.27000.4130135.9604world-music
1139991139992hETkH7cOfqmz3LqZDHZf5Cesária EvoraMiss PerfumadoBarbincor22241826False0.5260.4871-10.20400.07250.681000.0000000.08930.708079.1984world-music